Overview

ScaleOut Digital Twins™ provides a comprehensive user interface (UI) for deploying and managing digital twin models, connecting to data sources through popular event hubs or REST for streaming analytics, running digital twin simulations, and tracking aggregate statistics using graphical charts. The UI is designed make it easy to take advantage of digital twins, while allowing ScaleOut Digital Twins to take care of all the details required for high performance and scalability.

You can download and run the UI on-premises. Please see the following topic for details on installing the UI.

After deploying the UI, you are ready to build and deploy a digital twin model following these steps:

• Build a digital twin model in Java, C#, or using rules-based logic.

  Use the ScaleOut Digital Twin Builder Software Toolkit to create Java or C# models, as described in this topic. You can also create rules-engine or machine learning models with the Model Development Tool.

• Deploy the model using the UI, as described here.

  Upload the required binary files, typically as a zip file. The UI sends the files to the execution platform and prepares the model to receive messages from data sources. Log messages written by the model’s message processor are displayed in the UI.

• If you are using real-time digital twins for streaming analytics, connect to data sources as described here.

  For testing, you can send messages directly to the model from an application. You can then connect the model to supported event hubs or REST endpoints, such as Azure IoT Hub, by supplying the required connection parameters in the UI.

• If you are creating a simulation, configure and start it as described here.

  At least one digital twin model with simulation digital twins must be deployed to run a simulation. Simulation twins can also interact with real-time digital twins to model data sources and test streaming analytics scenarios.

• To automatically persist digital twin state, connect a persistence provider as described here.

  Connectors support both message hubs and persistence providers. ScaleOut Digital Twins supports SQL Server, SQLite, and integration with Microsoft Azure Digital Twins.

• Create charts to monitor aggregate statistics as described here.

  Charts aggregate and visualize properties across all digital twin instances for a model. You can configure the property, aggregation type (average, minimum, maximum, or count), optional group-by field, and chart type (bar, column, pie, or line). Charts refresh approximately every five seconds.

• Run queries to view properties for selected digital twin instances as described here.

  Queries can display results in tabular or geospatial formats and can run once or continuously.

• Monitor the deployment as described here.

  The UI displays model instance counts, message rates, memory usage, and simulation status. You can also inspect individual instances and track property changes over time.

After deploying your models, charts, and queries, the ScaleOut Digital Twins service automatically refreshes chart and continuous query results every few seconds using data-parallel computation. The execution engine scales across a cluster of servers to ensure fast processing.

The following sections provide a tour of the ScaleOut Digital Twins UI and explain its features in detail.